Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "245" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 34 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 34 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460017 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.032531 | 0.499682 | 0.742808 | -0.649048 | -0.104662 | -1.021878 | -1.815494 | 0.246717 | 0.5317 | 0.5295 | 0.3503 | nan | nan |
| 2460016 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.230750 | 0.537033 | 0.724512 | -0.592571 | -0.378387 | -1.318783 | -2.125988 | 0.065231 | 0.5387 | 0.5367 | 0.3530 | nan | nan |
| 2460015 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.287083 | 0.533907 | 0.793669 | -0.622190 | -0.276231 | -1.158639 | -2.301548 | -0.898555 | 0.5501 | 0.5461 | 0.3519 | nan | nan |
| 2460014 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.085995 | 0.623567 | 0.542511 | -0.712338 | -0.676739 | -0.933963 | -2.035093 | 0.299643 | 0.5239 | 0.5288 | 0.3559 | nan | nan |
| 2460013 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.341525 | 0.276062 | 0.831796 | -0.493627 | -0.070238 | -0.982402 | -2.297027 | -0.055324 | 0.5429 | 0.5463 | 0.3588 | nan | nan |
| 2460012 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.378266 | 0.271000 | 0.254730 | -1.164947 | -0.497464 | -0.848404 | -2.586506 | 0.497909 | 0.5475 | 0.5491 | 0.3538 | nan | nan |
| 2460011 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460010 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460009 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460008 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460007 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459999 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.6061 | 0.6006 | 0.3143 | nan | nan |
| 2459998 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.696917 | 0.231475 | 0.471040 | -1.046500 | -0.682456 | -0.932092 | -1.824659 | 0.797421 | 0.5691 | 0.5821 | 0.3807 | nan | nan |
| 2459997 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.857914 | 0.236934 | 0.746587 | -1.008250 | -0.032485 | -1.171204 | -2.374526 | 0.831788 | 0.5847 | 0.5981 | 0.3834 | nan | nan |
| 2459996 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.153805 | -0.133849 | 1.126935 | -0.495306 | 0.645015 | -1.136856 | -1.561094 | 0.678760 | 0.5915 | 0.6042 | 0.3956 | nan | nan |
| 2459995 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.955310 | 0.027253 | 0.652316 | -1.230792 | -0.262395 | -1.039302 | -1.397365 | 0.314837 | 0.5875 | 0.5994 | 0.3856 | nan | nan |
| 2459994 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.883085 | 0.271693 | 0.634583 | -1.227726 | -0.550830 | -1.186369 | -1.446702 | 0.103965 | 0.5812 | 0.5913 | 0.3811 | nan | nan |
| 2459993 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.458756 | 0.034190 | 1.318684 | -0.494088 | 0.957585 | -0.362357 | -1.512066 | -0.108740 | 0.5699 | 0.5958 | 0.3917 | nan | nan |
| 2459991 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.656222 | 0.661648 | 0.778096 | -1.061661 | -0.066437 | -1.331378 | -1.355786 | 0.354806 | 0.5817 | 0.5837 | 0.3882 | nan | nan |
| 2459990 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.308417 | 0.634557 | 1.243337 | -0.402119 | 0.411955 | -0.832446 | -1.870871 | -0.033584 | 0.5794 | 0.5862 | 0.3845 | nan | nan |
| 2459989 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.016681 | 0.483132 | 1.323675 | -0.405358 | 0.200656 | -1.309355 | -1.519066 | 0.109322 | 0.5775 | 0.5884 | 0.3875 | nan | nan |
| 2459988 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.466928 | 0.563383 | 1.259684 | -0.412707 | 0.676541 | -0.633345 | -1.570915 | -0.034279 | 0.5777 | 0.5905 | 0.3834 | nan | nan |
| 2459987 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.942188 | 0.244411 | 0.653880 | -1.208118 | -0.440697 | -1.117227 | -1.807667 | 0.025194 | 0.5852 | 0.5964 | 0.3765 | nan | nan |
| 2459986 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.585642 | 0.145610 | 1.249396 | -0.501294 | 0.313150 | -0.626005 | -0.707868 | -1.356926 | 0.6098 | 0.6273 | 0.3366 | nan | nan |
| 2459985 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.390586 | 0.008544 | 1.022225 | -0.673295 | 0.069553 | -1.355143 | -2.085956 | 0.359999 | 0.5854 | 0.5980 | 0.3850 | nan | nan |
| 2459984 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.356316 | 0.102003 | 1.099233 | -0.681841 | 0.764391 | -1.523677 | -1.428930 | -0.723039 | 0.5958 | 0.6123 | 0.3678 | nan | nan |
| 2459983 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.273831 | 0.595841 | 1.144291 | -0.433950 | -0.177943 | -0.796269 | -1.196286 | -1.080046 | 0.6070 | 0.6165 | 0.3399 | nan | nan |
| 2459982 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.744355 | 1.331831 | 0.471083 | -0.901626 | -1.180797 | -1.201019 | -0.939466 | -1.371762 | 0.6795 | 0.6732 | 0.2919 | nan | nan |
| 2459981 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.300239 | 2.056858 | -0.258331 | -1.398913 | -0.677184 | -1.114151 | -1.136937 | -0.044148 | 0.5818 | 0.5843 | 0.3784 | nan | nan |
| 2459980 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.350209 | 1.805550 | 0.031707 | -1.165357 | -0.753596 | -1.219080 | -0.401919 | -1.239223 | 0.6340 | 0.6375 | 0.3026 | nan | nan |
| 2459979 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.421110 | 2.124952 | 0.119536 | -1.098435 | -0.457670 | -1.431491 | -1.291139 | -0.527423 | 0.5744 | 0.5811 | 0.3780 | nan | nan |
| 2459978 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.430693 | 2.257307 | -0.317302 | -1.483721 | -0.739366 | -1.046093 | -1.386845 | -0.215104 | 0.5763 | 0.5807 | 0.3842 | nan | nan |
| 2459977 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.487217 | 1.849403 | 0.075885 | -1.097685 | -0.553202 | -1.092153 | -1.401843 | -0.392076 | 0.5365 | 0.5402 | 0.3433 | nan | nan |
| 2459976 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.402426 | 2.005971 | 0.121359 | -1.058057 | -0.609284 | -0.781062 | -1.135675 | -0.209497 | 0.5845 | 0.5891 | 0.3706 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Power | 0.742808 | 0.499682 | 0.032531 | -0.649048 | 0.742808 | -1.021878 | -0.104662 | 0.246717 | -1.815494 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Power | 0.724512 | 0.537033 | 0.230750 | -0.592571 | 0.724512 | -1.318783 | -0.378387 | 0.065231 | -2.125988 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Power | 0.793669 | 0.533907 | 0.287083 | -0.622190 | 0.793669 | -1.158639 | -0.276231 | -0.898555 | -2.301548 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Shape | 0.623567 | -0.085995 | 0.623567 | 0.542511 | -0.712338 | -0.676739 | -0.933963 | -2.035093 | 0.299643 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Power | 0.831796 | 0.341525 | 0.276062 | 0.831796 | -0.493627 | -0.070238 | -0.982402 | -2.297027 | -0.055324 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Temporal Discontinuties | 0.497909 | -0.378266 | 0.271000 | 0.254730 | -1.164947 | -0.497464 | -0.848404 | -2.586506 | 0.497909 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Temporal Discontinuties | 0.797421 | 0.696917 | 0.231475 | 0.471040 | -1.046500 | -0.682456 | -0.932092 | -1.824659 | 0.797421 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 0.857914 | 0.857914 | 0.236934 | 0.746587 | -1.008250 | -0.032485 | -1.171204 | -2.374526 | 0.831788 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 1.153805 | 1.153805 | -0.133849 | 1.126935 | -0.495306 | 0.645015 | -1.136856 | -1.561094 | 0.678760 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 0.955310 | 0.955310 | 0.027253 | 0.652316 | -1.230792 | -0.262395 | -1.039302 | -1.397365 | 0.314837 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 0.883085 | 0.883085 | 0.271693 | 0.634583 | -1.227726 | -0.550830 | -1.186369 | -1.446702 | 0.103965 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 1.458756 | 1.458756 | 0.034190 | 1.318684 | -0.494088 | 0.957585 | -0.362357 | -1.512066 | -0.108740 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 1.656222 | 1.656222 | 0.661648 | 0.778096 | -1.061661 | -0.066437 | -1.331378 | -1.355786 | 0.354806 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 1.308417 | 0.634557 | 1.308417 | -0.402119 | 1.243337 | -0.832446 | 0.411955 | -0.033584 | -1.870871 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Power | 1.323675 | 0.483132 | 1.016681 | -0.405358 | 1.323675 | -1.309355 | 0.200656 | 0.109322 | -1.519066 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 1.466928 | 0.563383 | 1.466928 | -0.412707 | 1.259684 | -0.633345 | 0.676541 | -0.034279 | -1.570915 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 0.942188 | 0.942188 | 0.244411 | 0.653880 | -1.208118 | -0.440697 | -1.117227 | -1.807667 | 0.025194 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 1.585642 | 0.145610 | 1.585642 | -0.501294 | 1.249396 | -0.626005 | 0.313150 | -1.356926 | -0.707868 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 1.390586 | 0.008544 | 1.390586 | -0.673295 | 1.022225 | -1.355143 | 0.069553 | 0.359999 | -2.085956 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Shape | 1.356316 | 1.356316 | 0.102003 | 1.099233 | -0.681841 | 0.764391 | -1.523677 | -1.428930 | -0.723039 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | ee Power | 1.144291 | 0.273831 | 0.595841 | 1.144291 | -0.433950 | -0.177943 | -0.796269 | -1.196286 | -1.080046 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Shape | 1.331831 | -0.744355 | 1.331831 | 0.471083 | -0.901626 | -1.180797 | -1.201019 | -0.939466 | -1.371762 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Shape | 2.056858 | 2.056858 | 0.300239 | -1.398913 | -0.258331 | -1.114151 | -0.677184 | -0.044148 | -1.136937 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Shape | 1.805550 | 1.805550 | 0.350209 | -1.165357 | 0.031707 | -1.219080 | -0.753596 | -1.239223 | -0.401919 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Shape | 2.124952 | 0.421110 | 2.124952 | 0.119536 | -1.098435 | -0.457670 | -1.431491 | -1.291139 | -0.527423 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Shape | 2.257307 | 2.257307 | 0.430693 | -1.483721 | -0.317302 | -1.046093 | -0.739366 | -0.215104 | -1.386845 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Shape | 1.849403 | 0.487217 | 1.849403 | 0.075885 | -1.097685 | -0.553202 | -1.092153 | -1.401843 | -0.392076 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 245 | N20 | RF_ok | nn Shape | 2.005971 | 2.005971 | 0.402426 | -1.058057 | 0.121359 | -0.781062 | -0.609284 | -0.209497 | -1.135675 |